Investigating and preventing scientific misconduct using Benford's Law

Integrity and trust in that integrity are fundamental to academic research. However, procedures for monitoring the trustworthiness of research, and for investigating cases where concern about possible data fraud have been raised are not well established. Here we suggest a practical approach for the investigation of work suspected of fraudulent data manipulation using Benford's Law. This should be of value to both individual peer-reviewers and academic institutions and journals. In this, we draw inspiration from well-established practices of financial auditing. We provide synthesis of the literature on tests of adherence to Benford's Law, culminating in advice of a single initial test for digits in each position of numerical strings within a dataset. We also recommend further tests which may prove useful in the event that specific hypotheses regarding the nature of data manipulation can be justified. Importantly, our advice differs from the most common current implementations of tests of Benford's Law. Furthermore, we apply the approach to previously-published data, highlighting the efficacy of these tests in detecting known irregularities. Finally, we discuss the results of these tests, with reference to their strengths and limitations.

Reliability of Financial Information from the Perspective of Benford's Law

This study investigates the conformity to Benford’s Law of the information disclosed in financial statements. Using the first digit test of Benford’s Law, the study analyses the reliability of financial information provided by listed companies on an emerging capital market before and after the implementation of International Financial Reporting Standards (IFRS). The results of the study confirm the increase of reliability on the information disclosed in the financial statements after IFRS implementation. The study contributes to the existing literature by bringing new insights into the types of financial information that do not comply with Benford’s Law such as the amounts determined by estimates or by applying professional judgment.

Evaluation of Large-scale Data to Detect Irregularity in Payment for Medical Services. An Extended Use of Benford's Law

BACKGROUND

Sophisticated anti-fraud systems for the healthcare sector have been built based on several statistical methods. Although existing methods have been developed to detect fraud in the healthcare sector, these algorithms consume considerable time and cost, and lack a theoretical basis to handle large-scale data.

OBJECTIVES

Based on mathematical theory, this study proposes a new approach to using Benford's Law in that we closely examined the individual-level data to identify specific fees for in-depth analysis.

METHODS

We extended the mathematical theory to demonstrate the manner in which large-scale data conform to Benford's Law. Then, we empirically tested its applicability using actual large-scale healthcare data from Korea's Health Insurance Review and Assessment (HIRA) National Patient Sample (NPS). For Benford's Law, we considered the mean absolute deviation (MAD) formula to test the large-scale data.

RESULTS

We conducted our study on 32 diseases, comprising 25 representative diseases and 7 DRG-regulated diseases. We performed an empirical test on 25 diseases, showing the applicability of Benford's Law to large-scale data in the healthcare industry. For the seven DRG-regulated diseases, we examined the individual-level data to identify specific fees to carry out an in-depth analysis. Among the eight categories of medical costs, we considered the strength of certain irregularities based on the details of each DRG-regulated disease.

CONCLUSIONS

Using the degree of abnormality, we propose priority action to be taken by government health departments and private insurance institutions to bring unnecessary medical expenses under control. However, when we detect deviations from Benford's Law, relatively high contamination ratios are required at conventional significance levels.

Benford's Law and articles of scientific journals: comparison of JCR® and Scopus data

Benford’s Law is a logarithmic probability distribution function used to predict the distribution of the first significant digits in numerical data. This paper presents the results of a study of the distribution of the first significant digits of the number of articles published of journals indexed in the JCR^® Sciences and Social Sciences Editions from 2007 to 2011. The data of these journals were also analyzed by the country of origin and the journal’s category. Results considering the number of articles published informed by Scopus are also presented. Comparing the results we observe that there is a significant difference in the data informed in the two databases.